{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a520191-e07f-4f77-9b6f-f0e40e941942",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">三  渐变圆角柱形图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd46cb18-69ce-418f-9153-0f737a01ddb6",
   "metadata": {},
   "source": [
    "# 一.筛选数据\n",
    "### 根据ERP订单数据文件的数据，筛选出裙子，毛衣，卫衣，裤子，夹克，外套，T恤，衬衫的订单数量。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ea0c3ed9-ddd5-44d2-a655-e7b0192c65a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "ERP订单数据 - 商品类别订单数量统计\n",
      "========================================\n",
      "  裙子: 120 个订单\n",
      "  毛衣: 107 个订单\n",
      "  卫衣: 127 个订单\n",
      "  裤子: 134 个订单\n",
      "  夹克: 126 个订单\n",
      "  外套: 123 个订单\n",
      "  T恤: 134 个订单\n",
      "  衬衫: 129 个订单\n",
      "========================================\n",
      "  总计: 1000 个订单\n",
      "原始数据总订单数: 1000 条\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# ---------------------- 1. 数据读取与统计 ----------------------\n",
    "data=pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "\n",
    "target_categories = {\n",
    "    \"裙子\": \"裙子\",\n",
    "    \"毛衣\": \"毛衣\",\n",
    "    \"卫衣\": \"卫衣\",\n",
    "    \"裤子\": \"裤子\",\n",
    "    \"夹克\": \"夹克\",\n",
    "    \"外套\": \"外套\",\n",
    "    \"T恤\": \"T恤\",\n",
    "    \"衬衫\": \"衬衫\"\n",
    "}\n",
    "\n",
    "# 统计各类别订单数量\n",
    "category_counts = {}\n",
    "for category, keyword in target_categories.items():\n",
    "    # 筛选\"商品名称\"中包含对应关键词的订单，统计数量\n",
    "    count = data[data[\"商品名称\"].str.contains(keyword, na=False)].shape[0]\n",
    "    category_counts[category] = count\n",
    "\n",
    "# \n",
    "#打印统计结果（格式化输出，清晰展示）\n",
    "print(\"=\" * 40)\n",
    "print(\"ERP订单数据 - 商品类别订单数量统计\")\n",
    "print(\"=\" * 40)\n",
    "for category, count in category_counts.items():\n",
    "    print(f\"{category:>4}: {count:>3} 个订单\")  # 右对齐，排版更整齐\n",
    "print(\"=\" * 40)\n",
    "# 可选：打印总计（验证统计完整性）\n",
    "total_count = sum(category_counts.values())\n",
    "print(f\"{'总计':>4}: {total_count:>3} 个订单\")\n",
    "print(f\"{'原始数据总订单数':>4}: {data.shape[0]:>3} 条\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199f237a-70be-4a17-a5bc-dc148cfaed08",
   "metadata": {},
   "source": [
    "# 二.可视化\n",
    "### 使用以上筛选的数据，生成一个渐变圆角柱形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1578e89d-0eb9-42df-92d1-0b65045b6823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---------------------- 2. 要求的柱形图 ----------------------\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 创建图形和坐标轴\n",
    "fig, ax = plt.subplots(figsize=(14, 9))\n",
    "\n",
    "# 设置深蓝色背景\n",
    "fig.patch.set_facecolor('#0a1c3d')\n",
    "ax.set_facecolor('#0a1c3d')\n",
    "\n",
    "# 准备数据\n",
    "categories = list(category_counts.keys())\n",
    "counts = list(category_counts.values())\n",
    "\n",
    "# 找出最大和最小销量的商品\n",
    "max_count = max(counts)\n",
    "min_count = min(counts)\n",
    "max_category = categories[counts.index(max_count)]\n",
    "min_category = categories[counts.index(min_count)]\n",
    "\n",
    "# 创建柱形图位置\n",
    "x_pos = np.arange(len(categories))\n",
    "\n",
    "# 创建每个柱子的渐变效果（从下往上深蓝到浅蓝）\n",
    "bar_width = 0.6\n",
    "for i, (category, count) in enumerate(zip(categories, counts)):\n",
    "    # 创建多个细条来模拟渐变效果\n",
    "    segments = 50  # 渐变细分段数\n",
    "    segment_height = count / segments\n",
    "    \n",
    "    # 定义颜色渐变（从深蓝到浅蓝）\n",
    "    colors = plt.cm.Blues(np.linspace(0.8, 0.3, segments))\n",
    "    \n",
    "    # 绘制渐变柱子\n",
    "    for j in range(segments):\n",
    "        bottom = j * segment_height\n",
    "        top = (j + 1) * segment_height\n",
    "        \n",
    "        # 创建一个小矩形段\n",
    "        segment = mpatches.Rectangle(\n",
    "            (i - bar_width/2, bottom),  # (x, y) 左下角坐标\n",
    "            bar_width,                  # 宽度\n",
    "            segment_height,             # 高度\n",
    "            facecolor=colors[j],        # 渐变色\n",
    "            edgecolor='none'            # 无边框\n",
    "        )\n",
    "        ax.add_patch(segment)\n",
    "    \n",
    "    # 在柱形顶端添加一条竖直线\n",
    "    line = mpatches.Rectangle(\n",
    "        (i - 0.02, count),          # (x, y) 左下角坐标\n",
    "        0.04,                       # 宽度（很窄）\n",
    "        max(counts)*0.03,           # 高度\n",
    "        facecolor='white',          # 白色\n",
    "        edgecolor='none'            # 无边框\n",
    "    )\n",
    "    ax.add_patch(line)\n",
    "    \n",
    "    # 在竖直线上方添加椭圆形数值标签\n",
    "    ellipse_label = Ellipse(\n",
    "        (i, count + max(counts)*0.09),      # 椭圆中心坐标\n",
    "        width=0.3,                          # 椭圆宽度（较小）\n",
    "        height=max(counts)*0.09,            # 椭圆高度（与原来矩形高度相同）\n",
    "        facecolor='#4a90e2',                # 蓝色背景\n",
    "        edgecolor='none'                    # 无边框\n",
    "    )\n",
    "    ax.add_patch(ellipse_label)\n",
    "    \n",
    "    # 在椭圆形标签中添加数值文本\n",
    "    ax.text(i, count + max(counts)*0.09, str(count), \n",
    "            ha='center', va='center', fontsize=11, color='white', fontweight='bold')\n",
    "\n",
    "# 设置坐标轴\n",
    "ax.set_xlim(-0.8, len(categories)-0.2)\n",
    "ax.set_ylim(0, max(counts) * 1.15)\n",
    "\n",
    "# 设置Y轴刻度\n",
    "y_max = max(counts) + 50\n",
    "y_step = y_max // 6 if y_max // 6 > 0 else 10\n",
    "y_ticks = np.arange(0, y_max + y_step, y_step)\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels([str(int(tick)) for tick in y_ticks], color='white', fontsize=10)\n",
    "\n",
    "# 设置X轴刻度\n",
    "ax.set_xticks(x_pos)\n",
    "ax.set_xticklabels(categories, color='white', fontsize=12, fontweight='bold')\n",
    "\n",
    "# 设置坐标轴标签\n",
    "ax.set_ylabel('订单数量', color='white', fontsize=14, fontweight='bold', labelpad=20)\n",
    "ax.set_xlabel('产品类别', color='white', fontsize=14, fontweight='bold', labelpad=20)\n",
    "\n",
    "# 隐藏边框\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "# 隐藏网格线\n",
    "ax.grid(False)\n",
    "\n",
    "# 添加标题和副标题\n",
    "plt.suptitle('3月商品销量对比', color='white', fontsize=22, fontweight='bold', y=0.95)\n",
    "plt.title(f'{max_category}订单最多，{min_category}订单最少，3月订单{total_count}', \n",
    "          color='white', fontsize=14, pad=30)\n",
    "\n",
    "# 添加数据来源说明\n",
    "ax.text(0.5, -0.12, '数据来源：ERP订单数据文件 ', \n",
    "        transform=ax.transAxes, ha='center', va='top', \n",
    "        color='lightgray', fontsize=11, alpha=0.8)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8ff55f-afcd-45f8-a09b-ffa68d76e856",
   "metadata": {},
   "source": [
    "# <font size=\"6\">3月商品销量数据分析报告</font>\n",
    "\n",
    "## <font size=\"5\">📊 数据概览</font>\n",
    "- <font size=\"4\">**总订单量**: 1,000单</font>\n",
    "- <font size=\"4\">**商品类别**: 8个主要品类</font>\n",
    "- <font size=\"4\">**数据来源**: ERP订单数据文件</font>\n",
    "- <font size=\"4\">**统计周期**: 2024年3月</font>\n",
    "\n",
    "## <font size=\"5\">📈 销售表现分析</font>\n",
    "\n",
    "### <font size=\"5\">销量排名</font>\n",
    "| <font size=\"4\">排名</font> | <font size=\"4\">商品类别</font> | <font size=\"4\">订单数量</font> | <font size=\"4\">占比</font> |\n",
    "|------|----------|----------|------|\n",
    "| <font size=\"4\">1️⃣</font> | <font size=\"4\">**裤子**</font> | <font size=\"4\">134单</font> | <font size=\"4\">13.4%</font> |\n",
    "| <font size=\"4\">1️⃣</font> | <font size=\"4\">**T恤**</font> | <font size=\"4\">134单</font> | <font size=\"4\">13.4%</font> |\n",
    "| <font size=\"4\">3️⃣</font> | <font size=\"4\">衬衫</font> | <font size=\"4\">129单</font> | <font size=\"4\">12.9%</font> |\n",
    "| <font size=\"4\">4️⃣</font> | <font size=\"4\">卫衣</font> | <font size=\"4\">127单</font> | <font size=\"4\">12.7%</font> |\n",
    "| <font size=\"4\">5️⃣</font> | <font size=\"4\">夹克</font> | <font size=\"4\">126单</font> | <font size=\"4\">12.6%</font> |\n",
    "| <font size=\"4\">6️⃣</font> | <font size=\"4\">外套</font> | <font size=\"4\">123单</font> | <font size=\"4\">12.3%</font> |\n",
    "| <font size=\"4\">7️⃣</font> | <font size=\"4\">裙子</font> | <font size=\"4\">120单</font> | <font size=\"4\">12.0%</font> |\n",
    "| <font size=\"4\">8️⃣</font> | <font size=\"4\">毛衣</font> | <font size=\"4\">107单</font> | <font size=\"4\">10.7%</font> |\n",
    "\n",
    "### <font size=\"5\">🔍 关键发现</font>\n",
    "\n",
    "#### <font size=\"5\">🏆 **热销品类**</font>\n",
    "- <font size=\"4\">**裤子和T恤并列第一**，各134单，占总销量的26.8%</font>\n",
    "- <font size=\"4\">这两类商品在3月份表现出强劲的市场需求</font>\n",
    "- <font size=\"4\">可能与春季换季和气温回升有关</font>\n",
    "\n",
    "#### <font size=\"5\">⚠️ **需关注品类**</font>\n",
    "- <font size=\"4\">**毛衣销量最低**，仅107单，比最高销量品类少27单</font>\n",
    "- <font size=\"4\">可能与3月份气温回暖，冬季服装需求下降有关</font>\n",
    "- <font size=\"4\">建议关注库存管理和促销策略调整</font>\n",
    "\n",
    "#### <font size=\"5\">📊 **销售分布特点**</font>\n",
    "- <font size=\"4\">各品类销量相对均衡，**最大差距仅27单**</font>\n",
    "- <font size=\"4\">除毛衣外，其他品类销量均在120-134单之间</font>\n",
    "- <font size=\"4\">整体销售结构**相对健康**，没有出现严重偏科现象</font>\n",
    "\n",
    "### <font size=\"5\">💡 业务建议</font>\n",
    "\n",
    "#### <font size=\"5\">短期策略</font>\n",
    "1. <font size=\"4\">**加大裤子和T恤的库存准备**，满足持续需求</font>\n",
    "2. <font size=\"4\">**对毛衣进行适当促销**，清理库存为春夏季做准备</font>\n",
    "3. <font size=\"4\">**保持衬衫、卫衣等中间品类稳定供应**</font>\n",
    "\n",
    "#### <font size=\"5\">长期规划</font>\n",
    "1. <font size=\"4\">**建立季节性销售预测模型**，提前调整采购计划</font>\n",
    "2. <font size=\"4\">**优化品类结构**，根据历史数据调整各品类占比</font>\n",
    "3. <font size=\"4\">**加强数据监控**，及时发现销售趋势变化</font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2650d7-b88b-4be3-9f2d-442c7c5656d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "607db0e8-be86-4ae2-860e-640823462884",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
